{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/why-should-i-trust-you-explaining-the","title":"\"Why Should I Trust You?\": Explaining the Predictions of Any Classifier","arxiv_id":"1602.04938","date":"2016-02-16","proceeding":null,"authors":["Marco Tulio Ribeiro","Sameer Singh","Carlos Guestrin"],"abstract":"Despite widespread adoption, machine learning models remain mostly black\nboxes. Understanding the reasons behind predictions is, however, quite\nimportant in assessing trust, which is fundamental if one plans to take action\nbased on a prediction, or when choosing whether to deploy a new model. Such\nunderstanding also provides insights into the model, which can be used to\ntransform an untrustworthy model or prediction into a trustworthy one. In this\nwork, we propose LIME, a novel explanation technique that explains the\npredictions of any classifier in an interpretable and faithful manner, by\nlearning an interpretable model locally around the prediction. We also propose\na method to explain models by presenting representative individual predictions\nand their explanations in a non-redundant way, framing the task as a submodular\noptimization problem. We demonstrate the flexibility of these methods by\nexplaining different models for text (e.g. random forests) and image\nclassification (e.g. neural networks). We show the utility of explanations via\nnovel experiments, both simulated and with human subjects, on various scenarios\nthat require trust: deciding if one should trust a prediction, choosing between\nmodels, improving an untrustworthy classifier, and identifying why a classifier\nshould not be trusted.","url_abs":"http://arxiv.org/abs/1602.04938v3","url_pdf":"http://arxiv.org/pdf/1602.04938v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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